CN108365900A - User access method based on energy consumption and pairing in super-intensive heterogeneous network system - Google Patents
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Abstract
The present invention provides the user access methods based on energy consumption and pairing in a kind of super-intensive heterogeneous network system.This method includes：The utility function value in each base station of user side to the user is calculated according to the SINR value between user and each base station, user is worth highest base station transmission access application to oneself utility function, the SINR value that base station extracts in applying from access sends the utility function value of the user of the access request as base station side, base station is worth the rate request of highest user according to base station side utility function and disturbed condition judges whether the highest user of the utility function value has access to the base station, if it is, the information being successfully accessed then is fed back to the highest user of utility function value；Otherwise, the information of access failure is fed back to the highest user of utility function value.The present invention proposes a kind of user's Access Algorithm under super-intensive deployment scenario, solves the problems, such as the user's access optimized based on efficiency under the conditions of given targeted rate.
Description
Technical Field
The invention relates to the technical field of wireless network communication, in particular to a user access method based on energy consumption and pairing in an ultra-dense heterogeneous network system.
Background
With the development of mobile communication technology, the wide use of smart phones, the rapid development of mobile internet, internet of things and various new services, the number of UEs (User Equipment) and mobile data traffic experience an explosive growth situation, and the ultra-dense deployment of heterogeneous networks will become an inevitable trend in the development of future mobile communication. Ultra-dense deployment of small cells will inevitably bring about an increase in network energy consumption and imbalance in base station load. The energy consumption of the base station is closely related to the load quantity related to the base station, so that a proper user access algorithm plays an important role in reducing the energy consumption of the base station and improving the user access fairness.
At present, a user access algorithm in the prior art focuses more on load balancing and system energy consumption. A user access algorithm in the prior art includes: under the condition of meeting the requirement of user data rate, an algorithm combining power and resource allocation is provided by taking optimization of base station transmitting power as a target. The drawback of this user access algorithm is that the total transmit power of the base station is not efficiently represented and optimized.
Disclosure of Invention
The embodiment of the invention provides a user access method based on energy consumption and pairing in a super-dense heterogeneous network system, which aims to overcome the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A user access method based on energy consumption and pairing in an ultra-dense heterogeneous network system comprises the following steps:
measuring and calculating the SINR value between the user and each base station by the user;
calculating utility function values of each base station to the user at the user side according to the SINR values between the user and each base station, and sending an access application to the base station with the highest utility function value by the user, wherein the access application carries channel quality information related to the SINR values between the user and the base station;
after receiving access applications sent by a plurality of users, a base station processes channel quality information provided by each user, takes an SINR value extracted from the access applications as a utility function value of the user sending the access request at a base station side, sorts the users according to the utility function value of the user at the base station side, judges whether the user with the highest utility function value can access the base station according to the rate request and the interference condition of the user with the highest utility function value at the base station side, and feeds back information of successful access to the user with the highest utility function value if the user with the highest utility function value can access the base station; otherwise, feeding back the information of access failure to the user with the highest utility function value.
Further, the said base station calculates the transmission power provided by the base station for each user according to the signal to interference plus noise ratio SINR between the users and the base station, including:
the relationship between the SINR between user i and base station j and the transmit power provided by base station j for user i is given by:
Pijtotal transmission power g of base station j when occupying all resource blocks for user i to transmitijIs the channel fading coefficient, g, of base station j to user iikFor the channel fading coefficient, σ, of base station k to user i2Is the power of white gaussian noise and is,is the maximum transmit power of base station j.
Further, the calculating a utility function value of each base station on the user side for the user according to the SINR value between the user and each base station includes:
the calculation formula of the utility function value of the base station j at the user side to the user i is as follows:
βtoffset values, SINR, corresponding to different types of base stationsijthe signal-to-interference-and-noise ratio of the base station j to the user i is t 1,2,3 respectively represent a macro base station, a pico base station and a home base station, α is a weighting factor, and L isjFor the load of the current jth base station,
further, the base station judges whether the user with the highest utility function value can access the base station according to the rate request and the interference condition of the user with the highest utility function value at the base station side, and if so, feeds back information of successful access to the user with the highest utility function value, including:
the base station takes the user with the highest utility function value at the base station side as a user to be accessed, recalculates the bandwidth allocation proportion of the user to be accessed and the original accessed user by adopting a Lagrange duality method and a binary search iteration method, judges that the user to be accessed is allowed to access the base station if the requirements of user rate and base station total power consumption optimization are met, feeds back successful access information to the user to be accessed, and simultaneously updates the current load of the base station.
Further, if not, feeding back the information of access failure to the user with the highest utility function value, including:
if the requirement of optimizing the user rate and the total power consumption of the base station is not met, the base station judges that the user to be accessed can not access the base station, the information of access failure is fed back to the user to be accessed, after the user to be accessed receives the information of access failure, the utility function value of the original application base station at the user side is set to be 0, and the user selects one base station with the largest utility function value in the rest base stations again to submit access application;
and repeating the process until all the users can access the base station, and stopping the user access algorithm.
Further, the recalculating the bandwidth allocation ratio of the user to be accessed and the original accessed user by using the lagrangian dual method and the binary search iteration method includes:
setting a user set as Z and the number as Z in the ultra-dense heterogeneous network system, setting a set of all base stations as M and the number as M, wherein a subscript i represents a user index, a subscript j represents a base station index, and a user is user equipment;
the binary access indicator variable is defined as:
supposing that the resource reuse factor of the ultra-dense heterogeneous network system is 1, bijAnd (3) setting the total resource block number of the jth base station as 50 and the minimum division ratio of the jth base station resource as 1/50 to 0.02 for the proportionality coefficient of the ith user occupying the total bandwidth of the jth base station, and satisfying the following conditions:
0＜bij≤1,bij＝0.02k,k∈N+(5)
setting the speed requirement fixed as R when all users communicate in the systemiAnd solving the transmission power provided by the base station j for the user i as follows:
when an access scheme is given, the total transmission power of the base station j is:
the Lagrange duality method and the binary search iteration method are adopted to solve the power consumption optimization formula shown in the following formula (9), and the proportionality coefficient b is calculatedijAnd a binary access indicator variable xij：
Solving equation (9) above requires satisfying the constraints of equations (1), (3), and (5).
Further, the power consumption optimization algorithm shown in the above formula (9) is solved by adopting a Lagrange dual method and a binary search iteration method, and the proportionality coefficient b is calculatedijAnd a binary access indicator variable xijThe method comprises the following steps:
given an access scheme, a binary access indicator variable xijWhen known, the formula (9) is converted into a proportionality coefficient b shown in the formula (10)ijThe optimization formula of (2):
substituting the transmission power P obtained by the formula (6)ijThen the above equation is converted to solve:
wherein Z isjFor the user set accessed by the jth base station, the above equations (11) and (12) need to satisfy the constraint conditions of the equations (1), (3) and (5);
equations (11) and (12) are solved by using lagrangian dual functions, and the lagrangian function of the jth base station is as follows:
and (3) solving a partial derivative of the Lagrangian function by using a KKT condition, and if the partial derivative is 0, solving:
the first derivative, f (b), is taken of the Lagrangian multiplier of equation (15)ij) In the domain range, λ is a monotone decreasing function about independent variable, and the optimal λ value is solved by a binary search iteration method, wherein the maximum value and the minimum value of λ are respectively λmaxAnd λminIt is shown that,satisfies the following conditions:
taking the independent variable satisfying the expressions (16) and (17) at the same timeSubstituting the formula (15) to obtain a series of values of lambda, namely:
then obtain
The independent variable obtained by the above formula (19) is taken as a valueSubstituting into the formula (15), and obtaining a series of lambda values in the same way, then:
wherein,and taking a value of the minimum lambda calculated by the ith user corresponding to the ith user in the jth base station. The interval divided by the maximum value and the minimum value of the calculated lambda can be used as the initial calculation range of the lambda in the binary search iteration method;
the process of solving the optimal lambda value by adopting a binary search iteration method is as follows: and performing the following verification process on all users accessing the jth base station according to the calculated bandwidth distribution proportion:
if b of all users accessing the jth base stationijThe sum of the addition is less than 1, which indicates that in the iteration process, the value of lambda is too large, and the subsequent iteration needs to make lambda be largermax＝λ(n)；
If b of all users accessing the jth base stationijThe sum of the addition is more than 1, which indicates that the value of lambda is too small in the iteration process, and the subsequent iteration needs to make lambda smallermin＝λ(n)；
Repeating the above iterative process continuously, i.e. making lambda(n+1)＝(λmax+λmin) And/2, continuously calculating the bandwidth distribution proportion corresponding to each user until the sum of the final addition is just 1, stopping the iterative process by the algorithm, and calculating the bandwidth distribution proportion b at the momentijI.e. the optimal solution meeting the minimum rate requirement of each user.
According to the technical scheme provided by the embodiment of the invention, the user access and energy efficiency optimization problems under the condition of the given target rate are researched, and on the basis of improving the total transmission power model of the base station, the user resource allocation proportion in the access process and the transmission power provided by the base station for each access user are optimized and adjusted by utilizing the Lagrange's even and binary method, so that the power consumption optimization model can more accurately reflect the actual transmission power and power consumption of the base station. The invention provides a user access algorithm used in an ultra-dense deployment scene, which solves the problem of user access based on energy efficiency optimization under the condition of a given target rate.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an ultra-dense heterogeneous cellular communication system according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a variation of a ratio of system throughput to total power consumption with a number of users according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating that a fairness factor of respective throughputs Jain of a system base station varies with a number of system users according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating that the fairness factor of the final access user number Jain of each base station of the system changes with the change of the system user number according to the embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Example one
The embodiment of the invention is improved on the basis of the existing literature, so that the power consumption optimization model can more accurately reflect the actual transmitting power and power consumption of the base station, and an improved access strategy based on the maximum signal-to-interference-and-noise ratio and the pairing theory is provided on the basis. The method comprises the steps of solving the load balancing problem caused by load quantity change in the access process by using a pairing theory, and optimizing and adjusting the resource proportion distributed to each user in the access process and the transmitting power provided by a base station for each user by using Lagrange couple and dichotomy.
System model and problem modeling
An ultra-dense deployment downlink heterogeneous cellular communication system is mainly considered in the embodiment of the present invention, and fig. 1 is a schematic view of an ultra-dense heterogeneous cellular communication system proposed in the embodiment of the present invention, as shown in fig. 1. The following types of base stations are present in the system: macro base stations, picocell base stations (Picocells), and home base stations (Femtocells). In the range studied, the set of all active users is recorded as Z, the number is Z, the set of all base stations is recorded as M, and the number is recorded as M. The indices i and j denote the active user index and the densely deployed base station index, respectively. In the embodiment of the present invention, the user refers to a user equipment.
The binary access indicator variable is defined as:
the SINR (Signal to Interference plus Noise Ratio) between user i and base station j is given by:
Pijtotal transmission power g of base station j when occupying all resource blocks for user i to transmitijIs the channel fading coefficient, g, of base station j to user iikFor the channel fading coefficient, σ, of base station k to user i2Is the power of white gaussian noise and is,is the maximum transmit power of base station j.
The embodiment of the invention adopts the maximum value of the interference suffered by the user to simplify the calculation of the interference process, and setsIs the sum of the maximum interference power experienced by the corresponding user. According to the shannon formula, the actual achievable communication rate of the user is:
ri＝Bbijlog2(1+SINRij) (4)
where B is the total bandwidth of the system.
Assume that the resource reuse factor of the system is 1. bijThe proportion coefficient of the ith user occupying the total bandwidth of the jth base station is set as 50 total resource block number, so that the minimum division proportion is 1/50 ═ 0.02, namely, the following requirements are met:
0＜bij≤1,bij＝0.02k,k∈N+(5)
setting the required rate of user communication in the system to be fixed as RiFor convenience of calculation, assuming that the required rates of all users are the same, the transmission power provided by the base station j to the user i is obtained as follows:
when an access scheme is given, the total transmission power of the base station j is:
likewise, PjThe constraint limits of equation (3) should also be satisfied.
The transmitting power of the base station is not the total power consumed by the base station, and the transmitting power model of the base station is a linear consumption model and mainly takes the following form:
wherein,for the final overall power consumption value of the base station,for a fixed power consumption value, Δ, of a base station of the corresponding typejSlope of the corresponding type base station in the linear consumption model.
Since the total power consumption of the base station depends directly on the final total transmit power of the base station, the optimization of power consumption is equivalent to the following optimization problem:
the above formula also needs to satisfy the restriction requirements of the formulas (1), (3) and (5). Since there are two types of optimization variables, it represents the strong coupling of the user access sub-problem and the bandwidth allocation sub-problem in the optimization problem. Wherein, solving for variable xijThe problem of (2) is that the user accesses the subproblem, and the variable b is solvedijThe problem of (2) is a bandwidth allocation sub-problem. The invention respectively carries out modeling solution on the two sub-problems and finally provides a user access algorithm based on energy consumption and pairing theory.
User access algorithm based on energy consumption and pairing theory in ultra-dense heterogeneous network
Sub-problem of bandwidth allocation
When given access scheme i.e. (binary access indicator variable x)ijKnown), the original optimization problem is changed into:
substituting the transmission power P obtained by the formula (6)ijThen the above equation is converted to solve:
wherein Z isjFor the user set accessed by the jth base station, the above equations (11) and (12) need to satisfy the constraints of equations (1), (3) and (5).
The optimization function solved by the embodiment of the invention adds the resource allocation proportion bijThe product term of (2). Due to PijIt is assumed that the total transmit power of the user after occupying all frequency resources of the corresponding base station does not represent the transmit power finally obtained by the user at the base station.
Thus, using PijbijThe transmission power obtained by the user i after obtaining part of the resources of the base station j can be correctly reflected. It can be proved that the optimization problem of the formula (10) is a convex optimization problem, and the sub-problem has only a minimum value point within the definition domain of the optimization variable. Taking out the part of the jth base station and marking as SjThe solution can be performed using a lagrange dual function. The lagrangian function for the jth base station is as follows:
by using the KKT condition, the partial derivative of the lagrangian function is calculated and made to be 0, then:
the KKT condition refers to that for an optimization problem with equality and inequality constraints, after constructing a corresponding lagrangian function, the optimal value of the function must satisfy the following three conditions, i.e., the KKT condition: 1. the function value of the function at the point after the constructed Lagrange function calculates the partial derivative of each independent variable is 0; 2. the function value of the equality constraint function brought to the optimal point is 0; 3. the function value of a new function formed by linear combination of inequality constraint functions at the point is 0.
In particular in this context, the optimal argument must satisfy the three conditions described above, for the first one, the corresponding partial derivative function needs to be calculated. For the second, which is not considered here for the moment, the dependency is defined by the following iterative selection of the value of λ. For the third, there is no inequality constraint in the (13) equation, so it is not considered.
And solving a first derivative of the Lagrangian function of the formula, wherein the first derivative value is less than 0. The calculation process is not listed. So f (b)ij) λ is a monotonically decreasing function of the argument within the domain. That is, if a suitable value of λ can be obtained, the optimal solution of the sub-problem of bandwidth allocation can be obtained. The embodiment of the invention adopts a binary search iteration method to solve the optimal lambda value. The maximum value and the minimum value of lambda are respectively lambdamaxAnd λminAnd (4) showing.Satisfies the following conditions:
taking the independent variable satisfying the expressions (16) and (17) at the same timeSubstituting into formula (15) to obtain a series ofAnd lambda is taken as:
then can obtain
Taking the independent variable obtained by the above formulaSubstituting into equation (15), a series of λ values can be obtained as well, then:
wherein,and taking a value of the minimum lambda calculated by the ith user corresponding to the ith user in the jth base station. The interval divided by the maximum value and the minimum value of the calculated lambda can be used as the initial calculation range of the lambda in the binary search iteration method.
The process of solving the optimal lambda value by adopting a binary search iteration method in the embodiment of the invention is as follows: and performing the following verification process on all users accessing the jth base station according to the calculated bandwidth distribution proportion:
if the sum of the addition is less than 1, the lambda is over-large in the iteration process, and the subsequent iteration needs to make lambda be largermax＝λ(n)；
If the sum of the sums is larger than 1, it indicates that in the iteration process, the value of lambda is too small,subsequent iterations require let λmin＝λ(n)。
Repeating the above iterative process continuously, i.e. making lambda(n+1)＝(λmax+λmin) And/2, continuously calculating the corresponding bandwidth allocation proportion of each user until the sum of the final addition is just 1, stopping the iterative process by the algorithm, wherein the bandwidth allocation proportion at the moment is the optimal solution meeting the requirement of the lowest rate of each user, and according to the solved b of each user accessed to the base station jijThe total transmit power of base station j is calculated according to equation (7).
The above processes are to determine the bandwidth resource allocation and the transmission power calculation of the base station to each access user when the user accesses, and solve xijIs determined for the subsequent access subprocess.
Determination of utility function during user access
The utility function for the set of users is:
βtoffset values, SINR, corresponding to different types of base stationsijthe signal-to-interference-and-noise ratio of the base station j to the user i is t 1,2,3 respectively represent a macro base station, a pico base station and a home base station, α is a weighting factor, and L isjFor the load of the current jth base station,
the utility function of the set of base stations is defined as:
that is, by means of the equation (22), for a given base station, the utility function of the base station for each user is the actual SINR of that user, which is determinedNoise ratio SINRijIs a performance parameter that the base station can obtain when the user i submits an application to the base station j. In the subsequent access process, the base station performs preference ranking on all users applying for the base station according to the magnitude of the signal to interference plus noise ratio, obviously, if a certain user is determined not to be able to access the base station, the utility function value of the user is 0.
Concrete implementation process of user access algorithm
Measuring and calculating the SINR value between the user and each base station by the user;
and calculating the utility function value of each base station to the user at the user side according to the SINR value between the user and each base station, and sending an access application to the base station with the highest utility function value by the user, wherein the access application carries channel quality information related to the SINR value between the user and the base station.
After receiving access applications sent by a plurality of users, a base station processes channel quality information provided by each user, takes an SINR value extracted from the access applications as a utility function value of the user sending the access request at a base station side, sorts the users according to the utility function value of the user at the base station side, judges whether the user with the highest utility function value can access the base station according to the rate request and the interference condition of the user with the highest utility function value at the base station side, and feeds back information of successful access to the user with the highest utility function value if the user with the highest utility function value can access the base station; otherwise, feeding back the information of access failure to the user with the highest utility function value.
The base station takes the user with the highest utility function value at the base station side as a user to be accessed, recalculates the bandwidth allocation proportion of the user to be accessed and the original accessed user by adopting a Lagrange duality method and a binary search iteration method, judges that the user to be accessed is allowed to access the base station if the requirements of user rate and base station total power consumption optimization are met, namely the bandwidth proportion of each user is added to be 1, and updates the corresponding xijIs 1, and feeds back the information of successful access to the user to be accessed,and simultaneously, the base station updates the current load of the base station.
If the requirement of optimizing the user rate and the total power consumption of the base station is not met, the base station judges that the user to be accessed can not access the base station, the information of access failure is fed back to the user to be accessed, after the user to be accessed receives the information of access failure, the utility function value of the original application base station at the user side is set to be 0, and the user selects one base station with the largest utility function value in the rest base stations again to submit access application.
And repeating the process until all the users can access the base station, and stopping the user access algorithm.
And after receiving the feedback information of the base station, the user decides the action of the next access application. If the access application of a certain user is successful, the system removes the user from the user queue to be accessed; if the access application of a certain user is rejected, the user sets the utility of the original application base station to 0 according to the calculation rule of the utility function of the user, and then selects one of the rest base stations with the maximum utility function value to submit the access application. And repeating the process until all the users can access the base station, and stopping the algorithm.
Simulation results and analysis
Simulation setup
A macro base station is arranged in the simulation system, the coverage radius is 500m, the maximum transmitting power of the macro base station is 46dBm, 10 Pico base stations, 40 Femto base stations and corresponding users are uniformly distributed in the research range, and the maximum transmitting power is 35dBm and 20dBm respectively. Some parameters used for the specific simulation are shown in table 1 below.
Table 1 simulation parameter settings
In terms of base station power consumption, using the above-mentioned linear power consumption model, the power consumption model parameters for different types of base stations are shown in table 2 below:
TABLE 2 base station Linear Power consumption model parameters
Simulation performance index
The present invention uses the rate power consumption ratio, i.e. the power consumption efficiency, as a main evaluation index, which is defined as the ratio of the total throughput of the system to the total power consumption of all base stations in the system.
In the fairness part, Jain fairness factors are used as main evaluation indexes, and the calculation mode is as follows:
wherein r isnThe correlation performance of the nth user, N is the total number of all users in the system. In particular, in the present invention, the users mentioned in the above formula are replaced by base stations in the system, and the correlation performance serves the total throughput of all the access users and the number of the access users for each base station.
Analysis of simulation results
The algorithm provided by the invention is an algorithm (MTPI-SINR) based on pairing Theory and Power consumption improvement and accessed by utilizing maximum signal-to-interference-and-noise ratio, while the comparison algorithm is mainly based on the pairing Theory and Band allocation averaging (MTPAA-SINR) and based on the pairing Theory and Band allocation averaging and MTPAA-SINR (MTPM-SINR), namely an access and Power adjustment algorithm based on inaccurate transmission Power minimization and Power minimization combined with the pairing Theory and provided by the existing literature and an algorithm (MTPI-CH) based on the pairing Theory and Power consumption improvement and accessed by utilizing maximum channel gain. The following is a detailed analysis of the relevant simulation results.
Fig. 2 is a schematic diagram of a situation that a ratio of system throughput to total power consumption changes with the number of users according to an embodiment of the present invention, that is, a result that power consumption efficiency changes with the number of system users. From the overall trend, the value decreases with increasing independent variable, because the base station bandwidth resource is limited, so the final actual total throughput of the system shows a decreasing situation as the number of users increases. In the longitudinal direction, the performance of different algorithms varies on the premise of the same number of users of the system. It can be seen that the algorithm provided by the invention is excellent in power consumption efficiency and superior to the other three algorithms.
Fig. 3 is a schematic diagram of the variation of Jain fairness factors of respective throughputs of system base stations with the variation of the number of system users. From the overall trend, the value increases with increasing independent variable. After longitudinal comparison, the MTPI algorithm is found to be superior to the other two algorithms in throughput fairness on the basis of the same user number, no matter the MTPI algorithm is based on the maximum signal-to-interference-and-noise ratio or the maximum channel gain. The reason is that after the power consumption is improved, the transmitting power of each base station can be basically adjusted to be optimal, and because the throughput of each base station is not only related to the transmitting power of the base station, but also closely related to the transmitting power of other base stations, the total throughput of each base station is more uniform in bandwidth distribution in the algorithm of the invention, so that the power optimization is carried out, and the consistency and the fairness under the traditional power optimization condition (the transmitting power is not accurately adjusted) are higher.
Fig. 4 is a schematic diagram showing that the Jain fairness factor of the number of the final access users of each base station of the system changes with the number of the users of the system. Under the four algorithms, the values of the terms all show an ascending trend along with the increase of the independent variable. Fig. 4 shows that the MTPI algorithm is based on the maximum sir or the maximum channel gain, and the fairness of the base station access number is better than the other two algorithms. For reasons consistent with fig. 3.
In summary, the embodiments of the present invention have studied the user access and energy efficiency optimization problem under the condition of a given target rate. Firstly, aiming at the defects of the power optimization expression mode of the existing literature, on the basis of improving a total transmission power model of a base station, the lagrangian duality-to-duality and dichotomy are utilized to optimize and adjust the resource allocation proportion of users in the access process and the transmission power provided by the base station for each access user, so that the power consumption optimization model can more accurately reflect the actual transmission power and power consumption of the base station.
The invention provides a user access algorithm used in an ultra-dense deployment scene, which solves the problem of user access based on energy efficiency optimization under the condition of a given target rate. Simulation results show that the algorithm has good fairness and is superior to other comparison algorithms in power consumption efficiency.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A user access method based on energy consumption and pairing in an ultra-dense heterogeneous network system is characterized by comprising the following steps:
measuring and calculating the SINR value between the user and each base station by the user;
calculating utility function values of each base station to the user at the user side according to the SINR values between the user and each base station, and sending an access application to the base station with the highest utility function value by the user, wherein the access application carries channel quality information related to the SINR values between the user and the base station;
after receiving access applications sent by a plurality of users, a base station processes channel quality information provided by each user, takes an SINR value extracted from the access applications as a utility function value of the user sending the access request at a base station side, sorts the users according to the utility function value of the user at the base station side, judges whether the user with the highest utility function value can access the base station according to the rate request and the interference condition of the user with the highest utility function value at the base station side, and feeds back information of successful access to the user with the highest utility function value if the user with the highest utility function value can access the base station; otherwise, feeding back the information of access failure to the user with the highest utility function value.
2. The method of claim 1, wherein the base station calculates the transmit power provided by the base station for each user according to the signal to interference plus noise ratio (SINR) between the user and the base station, and comprises:
the relationship between the SINR between user i and base station j and the transmit power provided by base station j for user i is given by:
Pijtotal transmission power g of base station j when occupying all resource blocks for user i to transmitijIs the channel fading coefficient, g, of base station j to user iikFor the channel fading coefficient, σ, of base station k to user i2Is the power of white gaussian noise and is,is the maximum transmit power of base station j.
3. The method of claim 1, wherein the calculating a utility function value for each base station on the user side for each user according to the SINR value between the user and each base station comprises:
the calculation formula of the utility function value of the base station j at the user side to the user i is as follows:
βtoffset values, SINR, corresponding to different types of base stationsijthe signal-to-interference-and-noise ratio of the base station j to the user i is t 1,2,3 respectively represent a macro base station, a pico base station and a home base station, α is a weighting factor, and L isjFor the load of the current jth base station,
4. the method according to claim 1,2 or 3, wherein the base station determines whether the user with the highest utility function value can access the base station according to the rate request and the interference situation of the user with the highest utility function value at the base station side, and if so, feeds back the information of successful access to the user with the highest utility function value, including:
the base station takes the user with the highest utility function value at the base station side as a user to be accessed, recalculates the bandwidth allocation proportion of the user to be accessed and the original accessed user by adopting a Lagrange duality method and a binary search iteration method, judges that the user to be accessed is allowed to access the base station if the requirements of user rate and base station total power consumption optimization are met, feeds back successful access information to the user to be accessed, and simultaneously updates the current load of the base station.
5. The method of claim 4, wherein the otherwise feeding back the information of access failure to the user with the highest utility function value comprises:
if the requirement of optimizing the user rate and the total power consumption of the base station is not met, the base station judges that the user to be accessed can not access the base station, the information of access failure is fed back to the user to be accessed, after the user to be accessed receives the information of access failure, the utility function value of the original application base station at the user side is set to be 0, and the user selects one base station with the largest utility function value in the rest base stations again to submit access application;
and repeating the process until all the users can access the base station, and stopping the user access algorithm.
6. The method according to claim 4, wherein the recalculating the bandwidth allocation ratio between the user to be accessed and the original accessed user by using the lagrangian dual method and the binary search iteration method comprises:
setting a user set as Z and the number as Z in the ultra-dense heterogeneous network system, setting a set of all base stations as M and the number as M, wherein a subscript i represents a user index, a subscript j represents a base station index, and a user is user equipment;
the binary access indicator variable is defined as:
supposing that the resource reuse factor of the ultra-dense heterogeneous network system is 1, bijAnd (3) setting the total resource block number of the jth base station as 50 and the minimum division ratio of the jth base station resource as 1/50 to 0.02 for the proportionality coefficient of the ith user occupying the total bandwidth of the jth base station, and satisfying the following conditions:
0＜bij≤1,bij＝0.02k,k∈N+(5)
setting the speed requirement fixed as R when all users communicate in the systemiAnd solving the transmission power provided by the base station j for the user i as follows:
when an access scheme is given, the total transmission power of the base station j is:
the Lagrange duality method and the binary search iteration method are adopted to solve the power consumption optimization formula shown in the following formula (9), and the proportionality coefficient b is calculatedijAnd a binary access indicator variable xij：
Solving equation (9) above requires satisfying the constraints of equations (1), (3), and (5).
7. The method as claimed in claim 6, wherein the power consumption optimization algorithm shown in the above formula (9) is solved by adopting a Lagrangian dual method and a binary search iteration method to calculate the proportionality coefficient bijAnd a binary access indicator variable xijThe method comprises the following steps:
given an access scheme, a binary access indicator variable xijWhen known, the formula (9) is converted into a proportionality coefficient b shown in the formula (10)ijThe optimization formula of (2):
substituting the transmission power P obtained by the formula (6)ijThen the above equation is converted to solve:
wherein Z isjFor the user set accessed by the jth base station, the above equations (11) and (12) need to satisfy the constraint conditions of the equations (1), (3) and (5);
equations (11) and (12) are solved by using lagrangian dual functions, and the lagrangian function of the jth base station is as follows:
and (3) solving a partial derivative of the Lagrangian function by using a KKT condition, and if the partial derivative is 0, solving:
the first derivative, f (b), is taken of the Lagrangian multiplier of equation (15)ij) In the domain range, λ is a monotone decreasing function about independent variable, and the optimal λ value is solved by a binary search iteration method, wherein the maximum value and the minimum value of λ are respectively λmaxAnd λminIt is shown that,satisfies the following conditions:
taking the independent variable satisfying the expressions (16) and (17) at the same timeSubstituting the formula (15) to obtain a series of values of lambda, namely:
then obtain
The independent variable obtained by the above formula (19) is taken as a valueSubstituting into the formula (15), and obtaining a series of lambda values in the same way, then:
wherein λ isi minAnd taking a value of the minimum lambda calculated by the ith user corresponding to the ith user in the jth base station. The interval divided by the maximum value and the minimum value of the lambda obtained by the calculation can be used as the initial calculation range of the lambda in the binary search iteration method;
the process of solving the optimal lambda value by adopting a binary search iteration method is as follows: and verifying all users accessing the jth base station according to the calculated bandwidth distribution proportion as follows:
if b of all users accessing the jth base stationijThe sum of the addition is less than 1, which indicates that in the iteration process, the value of lambda is too large, and the subsequent iteration needs to make lambda be largermax＝λ(n)；
If b of all users accessing the jth base stationijThe sum of the addition is more than 1, which indicates that the value of lambda is too small in the iteration process, and the subsequent iteration needs to make lambda smallermin＝λ(n)；
Repeating the above iterative process continuously, i.e. making lambda(n+1)＝(λmax+λmin) And/2, continuously calculating the bandwidth distribution proportion corresponding to each user until the sum of the final addition is just 1, stopping the iterative process by the algorithm, and calculating the bandwidth distribution proportion b at the momentijI.e. the optimal solution meeting the minimum rate requirement of each user.
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